The present invention relates to recognition, analysis, and classification of patterns in data from real world sources, events and processes. Patterns exist throughout the real world. Patterns also exist in the data used to represent or convey or store information about real world objects or events or processes. As information systems process more real world data, there are mounting requirements to build more sophisticated, capable and reliable pattern recognition systems.
Existing pattern recognition systems include statistical, syntactic and neural systems. Each of these systems has certain strengths which lends it to specific applications. Each of these systems has problems which limit its effectiveness.
Existing pattern recognition systems include statistical, syntactic and neural systems. Each of these systems has certain strengths which lends it to specific applications. Each of these systems has problems which limit its effectiveness.
Some real world patterns are purely statistical in nature. Statistical and probabilistic pattern recognition works by expecting data to exhibit statistical patterns. Pattern recognition by this method alone is limited. Statistical pattern recognizers cannot see beyond the expected statistical pattern. Only the expected statistical pattern can be detected.
Syntactic pattern recognizers function by expecting data to exhibit structure. While syntactic pattern recognizers are an improvement over statistical pattern recognizers, perception is still narrow and the system cannot perceive beyond the expected structures. While some real world patterns are structural in nature, the extraction of structure is unreliable.
Pattern recognition systems that rely upon neural pattern recognizers are an improvement over statistical and syntactic recognizers. Neural recognizers operate by storing training patterns as synaptic weights. Later stimulation retrieves these patterns and classifies the data. However, the fixed structure of neural pattern recognizers limits their scope of recognition. While a neural system can learn on its own, it can only find the patterns that its fixed structure allows it to see. The difficulties with this fixed structure are illustrated by the well-known problem that the number of hidden layers in a neural network strongly affects its ability to learn and generalize. Additionally, neural pattern recognition results are often not reproducible. Neural nets are also sensitive to training order, often require redundant data for training, can be slow learners and sometimes never learn. Most importantly, as with statistical and syntactic pattern recognition systems, neural pattern recognition systems are incapable of discovering truly new knowledge.
Accordingly, there is a need for an improved method and apparatus for pattern recognition, analysis, and classification which is not encumbered by preconceptions about data or models.